Monthly Archives: February 2016

NumPy and SciPy rely on BLAS and LAPACK for basic linear algebra functionality like matrix-vector multiplication, linear system solves, or routines for eigenvalue computation. The Intel Math Kernel Library (MKL) is a mathematics library providing amongst other things fast and multithreaded implementations of BLAS and LAPACK. In this blog post, I describe how to compile NumPy and SciPy with the Intel compilers using Intel MKL on Linux. Note NumPy and SciPy can be linked to MKL without the Intel compilers by providing the proper linker options to, e.g., GCC, and I will briefly explain this as well.

Yesterday I gave a talk on the preliminary results of my master's thesis on Projection Methods for Generalized Eigenvalue Problems. Below is the announcement for the talk; it mentions only complex (Hermitian) matrices but all results are immediately applicable to real matrices. The slides are here (PDF).

Projection Methods for Generalized Eigenvalue Problems

We will present properties and origins of such GEPs. We will also address quickly computable and structure preserving backward error bounds for these kinds of GEPs. There is an abundance of literature on backward error measures possessing one of these features but only recently, the author came across a backward error providing both.

We will elaborate on dense solvers for GEPs with HPSD matrices. The standard solver for GEPs with Hermitian matrices is fast but requires positive definite mass matrices and is only conditionally backward stable; the QZ algorithm for general GEPs is backward stable but it is also magnitudes slower and does not preserve any problem properties. In the talk, we will present two new backward stable and structure preserving solvers, one using deflation, the other one using the generalized singular value decomposition (GSVD). In comparison to the QZ algorithm, both solvers are competitive with the standard solver in our tests.